Characteristics and comparisons of acute stroke in “recovered" to “active COVID-19 and “pre-pandemic” in Qatar database
Bibliographic record
Abstract
Understanding the relationship of COVID-19 to stroke is important. We compare characteristics of pre-pandemic stroke (PPS), cases in acute COVID infection (CS) and in patients who have recovered from COVID-19 infection (RCS). We interrogated the Qatar stroke database for all stroke admissions between Jan 2020 and Feb 2021 (PPS) to CS and RCS to determine how COVID-19 affected ischemic stroke sub-types, clinical course, and outcomes prior to, during and post-pandemic peak. There were 3264 cases admitted (pre-pandemic: 3111, stroke in COVID-19: 60 and recovered COVID-19 stroke: 93). Patients with CS were significantly younger, had more severe symptoms, fever on presentation, more ICU admissions and poor stroke recovery at discharge when compared to PPS and RCS. Large vessel disease and cardioembolic disease was significantly higher in CS compared to PPS or RCS. There was a significant decline in stroke mimics in CS. Stroke in RCS has characteristics similar to PPS with no evidence of lasting effects of the virus on the short-term. However, CS is a more serious disease and tends to be more severe and have a poor prognosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".